1. Field of the Invention
The present invention relates to a white balance adjusting method and the device thereof, especially to a white balance adjusting method with scene detection and the device thereof.
2. Description of Related Art
Regarding an image capture device (e.g. a digital camera), the raw image it captured will show different colors due to different environment color temperatures. For instance, if the capture target is a white wall, the raw image will appear in a warm color under a low environment color temperature (e.g. 1000K to 2800K), but in a cold color under a high environment color temperature (e.g. 8000K to 12200K). Therefore, said image capture device usually performs a white balance process to the raw image, so as to avoid or reduce the influence to the color display under different environment color temperatures. In other words, the white balance process is to make the color of the image of the same target under different environment temperatures remains similar or the same to thereby eliminate the phenomena of color shift.
As to the present arts, there are two major white balance processing methods. One is called “gray word”; the other one is called “white point detection”. The theory of “gray word” is based on the assumption that an image normally includes a lot of colors. If an image randomly includes every kind of colors, the average of these colors should be gray theoretically. Accordingly, the algorithm of “gray word” is to sum up the colors of all sensing units in an image and calculate the average of the summation to obtain an average value, have a predetermined gray value divided by the average value to obtain a white balance gain if the average value is not equal to the gray value, and use the white balance gain to adjust the colors of all the sensing units in the image. However, said algorithm of “gray world” is only applicable to an image with full colors; if an image is mono-color or short of color variation (e.g. the image is a picture of sky or grassland), doing a white balance process with the “gray world” algorithm will cause color distortion to said image and lead to an unacceptable result (e.g. a sky image being dusky instead of blue, or a grassland image being gloomy instead of green).
On the other side, the theory of “white point detection” is to find out a plurality of sensing units whose color are similar to white in an image, sum up the colors of these sensing units and calculate the average of the summation to obtain an average value, have a predetermined white value divided by the average value to obtain a white balance gain if the average value is not equivalent to the white value, and use the white balance gain to adjust the colors of all the sensing units in the image. However, “white point detection” has its own problems. If the sensing units with colors similar to white are nowhere to be found in an image (e.g. an image of grass or sunset), doing a white balance process of “white point detection” will also cause color distortion to the image, and is incapable of restoring the correct or user-anticipated color of the image.
Since the two major white balance methods cannot restore the correct or user-anticipated color of an image related to a specific scene, the demand to improve white balance technique is still existed.